Opportunities for opioid overdose prediction: building a population health approach
Authors: Bennett Allen, Magdalena Cerdá
Journal: The Lancet Digital Health (2022)

The opioid overdose epidemic remains a profound public health crisis in the USA. In recent years, the introduction of potent synthetic opioids to the illicit drug supply and the disturbances to the health care and substance use disorder treatment systems during the COVID-19 pandemic have accelerated overdose deaths.1Cartus AR Li Y Macmadu A et al.Forecasted and observed drug overdose deaths in the US during the COVID-19 pandemic in 2020.JAMA Netw Open. 2022; 5e223418 Crossref PubMed Scopus (15) Google Scholar, 2Mattson CL Tanz LJ Quinn K Kariisa M Patel P Davis NL Trends and geographic patterns in drug and synthetic opioid overdose deaths—United States, 2013–2019.MMWR Morb Mortal Wkly Rep. 2021; 70: 202-207Crossref PubMed Google Scholar To meet these challenges, health-care professionals need new strategies. Predictive analytics could guide clinical decision making and inform health-care resource targeting. In a prognostic modelling study published in The Lancet Digital Health, Wei-Hsuan Lo-Ciganic and colleagues present the development and validation of a machine-learning algorithm to predict opioid overdose among Medicaid beneficiaries.3Lo-Ciganic WH Donohue JM Yang Q et al.Developing and validating a machine-learning algorithm to predict opioid overdose among Medicaid beneficiaries in two US states: a prognostic modeling study.Lancet Digit Health. 2022; 4: e455-e465Summary Full Text Full Text PDF PubMed Scopus (11) Google Scholar This study builds on a growing body of work at the intersection of medicine and data science—to which Lo-Ciganic and colleagues are leading contributors4Lo-Ciganic WH Huang JL Zhang HH et al.Evaluation of machine-learning algorithms for predicting opioid overdose risk among Medicare beneficiaries with opioid prescriptions.JAMA Netw Open. 2019; 2e190968 Crossref PubMed Scopus (113) Google Scholar, 5Lo-Ciganic WH Huang JL Zhang HH et al.Using machine learning to predict risk of incident opioid use disorder among fee-for-service Medicare beneficiaries: a prognostic study.PLoS One. 2020; 15e0235981 Crossref PubMed Scopus (18) Google Scholar, 6Guo J Lo-Ciganic WH Yang Q et al.Predicting mortality risk after a hospital or emergency department visit for nonfatal opioid overdose.J Gen Intern Med. 2021; 36: 908-915Crossref PubMed Scopus (2) Google Scholar—showing the utility of machine learning to inform clinical care for diverse patients with opioid use disorder or at risk for opioid overdose. The authors use data from 958 278 patients in Pennsylvania and 391 959 patients in Arizona with histories of opioid analgesic prescription. They restrict their sample to patients with opioid prescription histories, as clinicians might more efficiently focus overdose prevention interventions to this group compared with a general patient population. The study aimed to build a model using data from one state and validate it using data from a different state to demonstrate the portability of short-run clinical overdose prediction. The authors applied several commonly used classification models to their data. Results indicate that the authors' best performing model predicted future patient opioid overdose risk with high discrimination ability. Findings from external validation of their gradient boosting machines model showed that the model performed well in different time periods within the same state (C-statistic of 0·841 [95% CI 0·835–0·847] for Pennsylvania 2013–16, n=639 693; and 0·828 [0·822–0·834] for Pennsylvania 2017–18, n=318 585) and in a different state (0·817 [0·807–0·826] for Arizona 2015–17). This portable tool for providers who treat Medicaid populations fills a pressing gap in the literature.3Lo-Ciganic WH Donohue JM Yang Q et al.Developing and validating a machine-learning algorithm to predict opioid overdose among Medicaid beneficiaries in two US states: a prognostic modeling study.Lancet Digit Health. 2022; 4: e455-e465Summary Full Text Full Text PDF PubMed Scopus (11) Google Scholar The authors notably predict patient opioid overdose risk with high predictive performance over a short-run period. Prediction across an abbreviated, 3-month risk period has the potential for strong clinical effect. It can inform provider strategies to engage patients in effective medication treatment for opioid use disorder, increase co-prescription of naloxone with opioids or benzodiazepines, and refer patients to ancillary social and harm reduction services. Additionally, the authors' validation using Medicaid data from two states with distinct demographic and drug policy profiles is promising. Given the breadth of Medicaid in the USA, a portable overdose risk prediction model using these data presents a novel clinical opportunity for state and federal policy makers. However, further research should assess performance using data capturing risk after the COVID-19 pandemic and in regional contexts with drug supply and patient profiles that differ from the Northeast (Pennsylvania) and Southwest (Arizona), such as South, Midwest, and Pacific Coast. Likewise, researchers must consider the social and ethical implications of such work, as opioid overdose increasingly has been criminalised across the USA in the last several years.7Allen B Feldman JM Paone D Opinion: Public health and police: building ethical and equitable opioid responses.Proc Natl Acad Sci USA. 2021; 118e2118235118 Crossref Scopus (6) Google Scholar It is crucial that clinical algorithms guide health care and social service interventions without secondary harm. Latest advances in spatiotemporal machine learning have further shown the capacity for small-area prediction of opioid overdose mortality risk.8Neill DB Herlands W Machine learning for drug overdose surveillance.J Technol Hum Serv. 2018; 36: 8-14Crossref Scopus (8) Google Scholar As such, we see value in the use of predictive analytics for public health decision support. In the COVID-19 era more than ever, accurate and proactive resource targeting to high-risk geographies can help practitioners maximise preventive impact. Past work has illustrated the capacity for machine learning to identify characteristics of neighborhoods with elevated opioid overdose risk,9Schell RC Allen B Goedel WC et al.Identifying predictors of opioid overdose death at a neighborhood level with machine learning.Am J Epidemiol. 2022; 191: 526-533Crossref PubMed Scopus (10) Google Scholar and researchers are testing the use of predictive analytics for public health decision support.10Marshall BDL Alexander-Scott N Yedinak JL et al.Preventing overdose using information and data from the environment (PROVIDENT): protocol for a randomized, population-based, community intervention trial.Addiction. 2022; 117: 1152-1162Crossref PubMed Scopus (4) Google Scholar However, broader attention to prediction for population prevention is needed to realise the potential of machine learning in public health. Clinical and population health researchers have much to learn from one another regarding the use of machine learning to improve health outcomes. We commend Lo-Ciganic and colleagues for their model, which holds potential to prevent opioid overdose at the individual level.3Lo-Ciganic WH Donohue JM Yang Q et al.Developing and validating a machine-learning algorithm to predict opioid overdose among Medicaid beneficiaries in two US states: a prognostic modeling study.Lancet Digit Health. 2022; 4: e455-e465Summary Full Text Full Text PDF PubMed Scopus (11) Google Scholar Ending the opioid overdose epidemic is possible, and the shared toolkits of medicine, epidemiology, and data science have the potential to bend the curve through innovation and interdisciplinary engagement. This work was supported by the National Institute on Drug Abuse (T32 DA007233 to BA and R01 DA046620 to MC). Developing and validating a machine-learning algorithm to predict opioid overdose in Medicaid beneficiaries in two US states: a prognostic modelling studyA machine-learning algorithm predicting opioid overdose derived from Pennsylvania Medicaid data performed well in external validation with more recent Pennsylvania data and with Arizona Medicaid data. The algorithm might be valuable for overdose risk prediction and stratification in Medicaid beneficiaries. Full-Text PDF Open AccessTurning the tide on the opioid crisisIn February 2022, findings of the Stanford–Lancet Commission on the North American Opioid Crisis were published, presenting an analysis of seven key domains related to the crisis including regulatory failures, lack of addiction-related health and social care services, and the slow pace of innovation. Among the Commission's recommendations were promoting opioid stewardship (ie, risk-reduction interventions) such as prompts in electronic prescribing systems that nudge clinicians towards safer prescribing, and tasking a federal agency with leading innovative projects, including developing machine-learning algorithms to predict risk of addiction and overdose. Full-Text PDF Open Access